[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3429":3,"related-3429":80},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":75,"slug":78,"view_count":20,"doi":8,"paper":8,"created_at":79},3429,"安徽农科院作物所与阜康化肥、安徽朴茂签约'智能水凝胶肥料产品与技术开发应用'框架合作协议","https:\u002F\u002Fzws.aaas.org.cn\u002F4305187\u002F71181953.html","9月17日下午梅河口市阜康化肥有限公司、安徽朴茂农业科技有限公司一行来安徽省农科院作物所参加智能水凝胶肥料开发与应用项目合作交流会。作物所小麦栽培与耕作团队与安徽朴茂农业科技有限公司等单位联合研发的小麦专用生物膜保持型复合肥，先后以核心技术产品作为安徽省农业主推技术、农业农村部小麦大面积单产提升手册关键技术推介。三方共同签署《框架合作协议》，建立'科研设计—助剂配套—试验示范—产业化生产—市场推广'的协同合作机制。",null,"安徽省农业科学院 2026-09-20","2026-09-20T00:00:00Z","报道",10,false,64,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},16,17,13,12,6,1,"省级科研机构与化肥企业共建智能水凝胶肥料协同机制，属细分行业实质进展，但时效稍旧、影响层级有限。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","成果转化","小麦","产学研合作","新型肥料",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"F3","fertilizers","肥料与水肥一体化","\u002Ftopic\u002Finputs-equipment\u002Ffertilizers","F",[],{"status":39,"locked":13,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,57,64,70],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":50},11,true,[48],"肥料",[48],[],{"code":52,"score":53,"supported":46,"title":54,"abstract":55,"tags":56},"A2",4,[],[28],[28],{"code":58,"score":59,"supported":46,"title":60,"abstract":61,"tags":63},"A8",3,[],[62],"单产提升",[],{"code":65,"score":66,"supported":13,"title":67,"abstract":68,"tags":69},"G1",2,[],[],[29,27],{"code":71,"score":21,"supported":13,"title":72,"abstract":73,"tags":74},"I1",[],[],[26],[76,77],"安徽省农科院 智能水凝胶肥料","阜康化肥 安徽朴茂 小麦专用肥","安徽省农科院智能水凝胶肥料-3429","2026-09-25T00:09:32.131325Z",{"total":20,"page":21,"page_size":20,"items":81},[82,247,311,405,474,554],{"id":83,"title":84,"url":85,"summary":86,"summary_zh":87,"content":8,"source_name":88,"source_url":85,"published_at":89,"category":90,"cover_url":8,"hotness":12,"is_selected":13,"score":91,"score_detail":92,"sources":97,"tags":99,"primary_topic":103,"secondary_topics":107,"classification":115,"search_phrases":183,"slug":186,"view_count":59,"doi":187,"paper":188,"created_at":246},3759,"Synergistic interaction effects of precision phosphorus fertigation and residue management on productivity, profitability, and resource-use efficiency of wheat under conservation agriculture","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1944629","Phosphorus (P) management under conventional surface-broadcast application in calcareous soils of the Indo-Gangetic Plains (IGP) suffers from severe fixation losses, limiting phosphorus-use efficiency (PUE) to 10–25% and contributing to yield gaps in wheat, a globally important food security crops. A two-year (2023–24 and 2024–25) split-plot field experiment was conducted within a mid-term conservation agriculture (CA)-based maize-wheat-greengram system to evaluate nine phosphorus fertigation levels and split application schedules through subsurface drip fertigation (SSDF), combined with two residue management practices (retention: R+, no-retention: R-), on growth, yield, water productivity, economics, and resource-use efficiency and post-harvest available phosphorus of wheat (cv. HD 2967). Treatments included no-P (control), SSDF of P as DAP at 50, 75, and 100% of the recommended dose (RDP; 60 kg P 2 O 5 ha −1 ) applied either fully as basal (10 DAS) or split (10 and 25 DAS), along with a nano-DAP foliar spray, compared against conventional CA with flood irrigation and basal broadcast P (CA-FI-100% RDP-B). SSDF treatments consumed only 178–275 mm of total water input against 437–452 mm under flood irrigation. Among SSDF treatments, 100% RDP applied in two splits (100% RDP-2S) consistently recorded the highest growth attributes, grain yield (6.23 and 6.31 t ha −1 ), and economic returns (BCR: 1.8–2) across both years, improving grain yield by 10.6–11.5% and net returns by 13.7% over CA-FI-100% RDP-B. Notably, 75% RDP-2S was statistically comparable to 100% RDP-2S for yield and profitability, demonstrating that a 25% reduction in P dose under precision SSDF does not compromise productivity. Total and irrigation water productivity improved by 127.7 and 156.7%, respectively, under 100% RDP-2S over CA-FI-100% RDP-B. Residue retention further enhanced productivity, increasing grain yield by 14.0–15.0% and net returns by 26.4–27.5%. Post-harvest soil available P was 18.0 and 43.3% higher under SSDF than under CA-FI-100% RDP-B at 0–15 and 15–30 cm, respectively. The interaction of split P fertigation and residue retention was consistently superior for grain yield and water productivity. These findings establish 75–100% RDP delivered through split SSDF, combined with residue retention, as an agronomically superior, water-efficient, economically viable strategy for sustainable wheat intensification in the semi-arid IGPs.","在印度河-恒河平原（IGP）石灰性土壤上，常规地表撒施条件下的磷（P）管理面临严重的固定损失，导致磷利用效率（PUE）仅为10–25%，并造成小麦——一种全球重要的粮食安全作物——的产量差距。在中期保护性农业（CA）为基础的玉米-小麦-绿豆轮作体系中，开展了一项为期两年（2023–24和2024–25）的裂区田间试验，评估九种磷肥滴灌水平和分次施用方案通过地下滴灌施肥（SSDF）对小麦（品种HD 2967）生长、产量、水分生产力、经济效益、资源利用效率及收获后土壤有效磷的影响，并结合两种秸秆管理方式（还田：R+，不还田：R-）。处理包括：无磷（对照）、以DAP形式通过SSDF施用推荐施磷量（RDP；60 kg P₂O₅ ha⁻¹）的50%、75%和100%，分别以全部基施（播种后10天）或分次施用（播种后10天和25天），同时配合纳米DAP叶面喷施，并与常规保护性农业配合漫灌和基施撒施磷（CA-FI-100% RDP-B）进行比较。SSDF处理的总水分投入仅为178–275 mm，而漫灌条件下为437–452 mm。在SSDF处理中，100% RDP分两次施用（100% RDP-2S）在两年中均持续表现出最高的生长指标、籽粒产量（6.23和6.31 t ha⁻¹）和经济效益（BCR：1.8–2），较CA-FI-100% RDP-B分别提高籽粒产量10.6–11.5%和净收益13.7%。值得注意的是，75% RDP-2S在产量和盈利能力上与100% RDP-2S无统计学差异，表明在精准SSDF下减少25%的磷用量不会影响生产力。与CA-FI-100% RDP-B相比，100% RDP-2S处理下总水分生产力和灌溉水分生产力分别提高了127.7%和156.7%。秸秆还田进一步提高了生产力，使籽粒产量增加14.0–15.0%，净收益增加26.4–27.5%。收获后土壤有效磷在0–15 cm和15–30 cm土层中，SSDF较CA-FI-100% RDP-B分别高出18.0%和43.3%。分次磷肥滴灌与秸秆还田的交互作用在籽粒产量和水分生产力方面始终表现最优。这些研究结果表明，通过分次SSDF施用75–100% RDP并结合秸秆还田，是一种农艺上优越、水分高效、经济可行的策略，可用于半干旱印度河-恒河平原小麦的可持续集约化生产。","Frontiers in Sustainable Food Systems","2026-09-28T00:00:00Z","论文",80,{"impact":93,"substance":94,"depth":93,"authority":18,"freshness":95,"relevant":21,"comment":96},18,22,9,"两年田间试验证实分次地下滴灌施磷配合秸秆还田可减磷25%并显著提升小麦产量与水肥利用效率，数据扎实、结论可靠，对半干旱地区精准水肥管理有直接参考价值。",[98],{"name":88,"url":85},[26,100,28,101,102],"精准施肥","保护性耕作","水肥一体化",{"code":52,"slug":104,"name":28,"path":105,"parent_code":106},"wheat","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[108,109],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":110,"slug":111,"name":112,"path":113,"parent_code":114},"H8","sustainable-agriculture","可持续与保护性农业","\u002Ftopic\u002Fenvironment\u002Fsustainable-agriculture","H",{"status":39,"locked":13,"version":40,"method":41,"evidence":116},{"candidates":117,"agricultural_evidence":46},[118,124,129,134,141,147,154,161,167,173,179],{"code":32,"score":16,"supported":46,"title":119,"abstract":121,"tags":123},[120],"fertigation",[122,120],"施肥",[102,100],{"code":52,"score":125,"supported":46,"title":126,"abstract":127,"tags":128},15,[104],[28,104],[28],{"code":110,"score":19,"supported":46,"title":130,"abstract":132,"tags":133},[131],"conservation agriculture",[131],[101],{"code":135,"score":20,"supported":46,"title":136,"abstract":137,"tags":140},"A3",[],[138,139],"玉米","maize",[],{"code":58,"score":20,"supported":46,"title":142,"abstract":143,"tags":146},[],[144,145],"粮食安全","food security",[],{"code":148,"score":20,"supported":46,"title":149,"abstract":150,"tags":153},"H2",[],[151,152],"土壤","soil",[],{"code":155,"score":20,"supported":46,"title":156,"abstract":157,"tags":160},"H3",[],[158,159],"灌溉","irrigation",[],{"code":162,"score":59,"supported":46,"title":163,"abstract":164,"tags":166},"A7",[],[165],"轮作",[],{"code":168,"score":59,"supported":46,"title":169,"abstract":170,"tags":172},"H5",[],[171],"秸秆还田",[],{"code":174,"score":59,"supported":46,"title":175,"abstract":176,"tags":178},"H7",[],[177],"干旱",[],{"code":71,"score":21,"supported":13,"title":180,"abstract":181,"tags":182},[],[],[26],[184,185],"地下滴灌 磷肥 小麦","保护性耕作 秸秆还田 小麦","地下滴灌磷肥小麦-3759","10.3389\u002Ffsufs.2026.1944629",{"doi":187,"openalex_id":189,"authors":190,"venue":88,"cited_by_count":238,"oa_url":85,"card":239,"direction":243,"ingested_from":245},"W7214579192",[191,193,196,198,200,202,204,207,209,211,213,215,217,220,222,224,227,230,233,236],{"name":192,"orcid":8},"Kadagonda Nithinkumar",{"name":194,"orcid":195},"Anchal Dass","https:\u002F\u002Forcid.org\u002F0000-0003-3909-1803",{"name":197,"orcid":8},"Devika A. R.",{"name":199,"orcid":8},"Arjun Singh",{"name":201,"orcid":8},"Susama Sudhishri",{"name":203,"orcid":8},"Vinod Kumar Singh",{"name":205,"orcid":206},"Sanjay Singh Rathore","https:\u002F\u002Forcid.org\u002F0000-0001-6725-1478",{"name":208,"orcid":8},"Sarvendra Kumar",{"name":210,"orcid":8},"Teekam Singh",{"name":212,"orcid":8},"Jitendra Rajput",{"name":214,"orcid":8},"Pravin Kumar Upadhyaya",{"name":216,"orcid":8},"R. N. Sahoo",{"name":218,"orcid":219},"ARPULA SAIRAM","https:\u002F\u002Forcid.org\u002F0009-0009-9034-6032",{"name":221,"orcid":8},"Aye Aye San",{"name":223,"orcid":8},"K. S. Sachin",{"name":225,"orcid":226},"Mummasani Asritha","https:\u002F\u002Forcid.org\u002F0009-0002-4619-9943",{"name":228,"orcid":229},"Ramanjit Kaur","https:\u002F\u002Forcid.org\u002F0000-0001-5117-050X",{"name":231,"orcid":232},"Mona Nagargade","https:\u002F\u002Forcid.org\u002F0000-0003-1062-512X",{"name":234,"orcid":235},"Vishal Tyagi","https:\u002F\u002Forcid.org\u002F0000-0001-5593-3076",{"name":237,"orcid":8},"Subhash Babu",0,{"tldr":240,"method":241,"finding":242,"direction":243,"opportunity":244},"研究保护性农业下精准磷肥滴灌与秸秆还田对小麦产量、收益和资源效率的协同效应。","两年裂区田间试验，地下滴灌分次施磷与秸秆还田处理，测产量、水生产力与经济指标。","75–100%推荐磷量分次滴灌配秸秆还田可增产10%以上、节水超一倍且不减产。","智慧农业 \u002F 农业物联网","可探索基于土壤磷素实时监测的变量施磷决策模型，并与秸秆还田互作机制结合优化。","openalex","2026-09-29T23:30:06.859116Z",{"id":248,"title":249,"url":250,"summary":251,"summary_zh":252,"content":8,"source_name":253,"source_url":250,"published_at":254,"category":90,"cover_url":8,"hotness":12,"is_selected":13,"score":255,"score_detail":256,"sources":258,"tags":260,"primary_topic":263,"secondary_topics":264,"classification":272,"search_phrases":294,"slug":297,"view_count":66,"doi":298,"paper":299,"created_at":310},2332,"Wheat Nitrogen Fertilizer Management Using GreenSeeker Handheld Crop Canopy Sensor","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijaes.vol.11.no3.2025.pg12.22","One of the essential factors in increasing agricultural yields of cereal crops is increasing the grain yield without increasing production costs. Wheat (Triticum aestivum L.) is the main crop in the food basket of the entire world. Therefore, it is necessary to determine the appropriate nitrogen (N) requirements to obtain the optimal production of wheat crops by investigating the impact of different N levels on the wheat crop yield in the Al-Muthanna region, as well as determining the possibility of predicting grain yield using the GreenSeeker handheld proximal crop canopy sensorbased differences vegetative difference index (NDVI). Thus, there is a need to re-evaluate the previous recommendations using remote sensing techniques. The experimental treatments were five levels of N fertilizer including (0 Kg N ha-1 , 50 kg N ha-1 , 100 kg N ha-1 ,150 kg N ha-1, and 200 kg N ha-1, and each N level was divided into 100%, 70%, 50% percentages, respectively. This study was conducted at the Experiment Station and Agriculture Research of the College of AgricultureAl-Muthanna University. The NDVI measurements were obtained at FK5, FK7, and FK9 according to the Feekes scale growth stage. The results indicated significant differences in grain yield between different levels of N fertilizer and a 70% percentage outperformed on the 100% and 50% treatments for each of the two N levels of 200 and 150 kg ha-1 . The results also show significant differences between NDVI values for different N fertilizer levels. The NDVI readings and wheat yield values increased and followed a similar pattern with increasing N fertilizer levels. This suggests that NDVI can predict wheat grain yield when the NDVI is not saturated. This study showed the potential of using GreenSeeker proximal crop canopy sensor-based NDVI readings as a useful tool to predict wheat grain yield.","提高谷类作物农业产量的关键因素之一是在不增加生产成本的前提下提高谷物产量。小麦（Triticum aestivum L.）是全球粮食结构中的主要作物。因此，有必要通过研究不同氮水平对Al-Muthanna地区小麦作物产量的影响，确定获得小麦作物最佳产量所需的适宜氮（N）需求量，并确定利用基于GreenSeeker手持式近地作物冠层传感器的归一化植被差异指数（NDVI）预测谷物产量的可能性。因此，需要利用遥感技术重新评估以往的推荐方案。试验处理包括五个氮肥水平（0 kg N ha⁻¹、50 kg N ha⁻¹、100 kg N ha⁻¹、150 kg N ha⁻¹和200 kg N ha⁻¹），每个氮水平分别按100%、70%、50%的比例施用。本研究在Al-Muthanna大学农学院实验站与农业研究中心进行。NDVI测定根据Feekes尺度生育阶段在FK5、FK7和FK9时期进行。结果表明，不同氮肥水平间谷物产量存在显著差异，在200和150 kg ha⁻¹两个氮水平下，70%施用量处理均优于100%和50%处理。结果还显示，不同氮肥水平间NDVI值存在显著差异。随着氮肥水平的提高，NDVI读数和小麦产量值均增加并呈现相似的变化趋势。这表明在NDVI未饱和时，NDVI可以预测小麦谷物产量。本研究表明，利用基于GreenSeeker近地作物冠层传感器的NDVI读数作为预测小麦谷物产量的有用工具具有潜力。","INTERNATIONAL JOURNAL OF AGRICULTURE AND EARTH SCIENCE","2026-09-10T00:00:00Z",61,{"impact":19,"substance":93,"depth":125,"authority":12,"freshness":20,"relevant":21,"comment":257},"基于GreenSeeker手持冠层传感器NDVI预测小麦产量并优化氮肥用量，方法实用但属区域性田间试验，产业影响有限。",[259],{"name":253,"url":250},[26,100,28,261,262],"遥感","NDVI",{"code":52,"slug":104,"name":28,"path":105,"parent_code":106},[265,266],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"code":267,"slug":268,"name":269,"path":270,"parent_code":271},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":39,"locked":13,"version":40,"method":41,"evidence":273},{"candidates":274,"agricultural_evidence":46},[275,279,284,290],{"code":52,"score":125,"supported":46,"title":276,"abstract":277,"tags":278},[104],[28,104],[28],{"code":32,"score":19,"supported":46,"title":280,"abstract":282,"tags":283},[281],"fertilizer",[281],[100],{"code":267,"score":285,"supported":46,"title":286,"abstract":287,"tags":289},8,[],[261,288],"remote sensing",[262,261],{"code":71,"score":21,"supported":13,"title":291,"abstract":292,"tags":293},[],[],[26],[295,296],"智慧农业 精准施肥 小麦 遥感","智慧农业 精准施肥","智慧农业精准施肥小麦遥感-2332","10.56201\u002Fijaes.vol.11.no3.2025.pg12.22",{"doi":298,"openalex_id":300,"authors":301,"venue":253,"cited_by_count":238,"oa_url":8,"card":304,"direction":308,"ingested_from":245},"W7212289620",[302],{"name":303,"orcid":8},"Mohammed A. Naser",{"tldr":305,"method":306,"finding":307,"direction":308,"opportunity":309},"用GreenSeeker手持冠层传感器NDVI预测小麦产量并优化氮肥管理。","伊拉克田间试验，5个氮水平，Feekes 5\u002F7\u002F9期测NDVI。","NDVI与产量随氮肥增加同步上升，未饱和时可预测产量，70%氮量表现更优。","农业遥感与作物表型","可探索NDVI饱和条件下的替代植被指数及不同品种\u002F气候区的氮肥推荐模型迁移。","2026-09-13T23:30:26.260534Z",{"id":312,"title":313,"url":314,"summary":315,"summary_zh":316,"content":8,"source_name":317,"source_url":314,"published_at":318,"category":90,"cover_url":8,"hotness":12,"is_selected":13,"score":319,"score_detail":320,"sources":322,"tags":324,"primary_topic":328,"secondary_topics":329,"classification":331,"search_phrases":372,"slug":375,"view_count":238,"doi":376,"paper":377,"created_at":404},1725,"Sustainable Wheat Production Through Precision Agriculture: An LCA Case Study in Italy","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fland15091646","The growing demand for food is placing growing pressure on agricultural systems, raising concerns about their environmental sustainability. In this context, precision agriculture represents a promising approach for improving resource-use efficiency while maintaining high yields. This study aimed to assess the environmental performance of soft wheat production during the transition from conventional to smart and precision farming under real farm conditions. A Life Cycle Assessment (LCA) was conducted following ISO 14040 and 14044 standards using primary data collected from a farm located in Umbria (Italy). Two consecutive three-year production periods were compared: conventional farming (2019–2021) and smart and precision farming (2022–2024). Environmental impacts were assessed using the Environmental Footprint 3.1 method. The transition was associated with a 20.8% reduction in the overall environmental impact. This improvement reflects the combined effects of adopting smart and precision agriculture technologies together with the renewal of farm machinery, which results in lower fuel consumption and more efficient fertilizer and pesticide management. Most impact categories showed improvements, particularly climate change, ecotoxicity, and particulate matter. However, freshwater eutrophication increased due to changes in fertilization strategy, including reduced nitrogen fertilizer use and increased phosphorus fertilizer application. Sensitivity analyses confirmed the consistency of the results; despite some influence on yield variation, the overall comparison remained consistent under the tested scenarios. These findings highlight the potential of smart and precision agriculture to improve the environmental sustainability of soft wheat production under real farm conditions.","日益增长的粮食需求给农业系统带来了越来越大的压力，引发了对农业环境可持续性的担忧。在此背景下，精准农业（precision agriculture）代表了一种在保持高产的同时提高资源利用效率的有前景的方法。本研究旨在评估在真实农场条件下，软质小麦（soft wheat）生产从传统农业向智能与精准农业转型过程中的环境绩效。依据ISO 14040和ISO 14044标准，利用从意大利翁布里亚（Umbria）地区一个农场收集的一手数据，进行了生命周期评价（Life Cycle Assessment, LCA）。研究比较了两个连续的三生产周期：传统农业（2019—2021年）和智能与精准农业（2022—2024年）。采用环境足迹3.1方法（Environmental Footprint 3.1）评估环境影响。转型与总体环境影响降低20.8%相关联。这一改善反映了采用智能与精准农业技术以及农机更新的综合效应，从而降低了燃料消耗，并实现了肥料和农药的更高效管理。大多数影响类别均有所改善，尤其是气候变化、生态毒性和颗粒物。然而，淡水富营养化（freshwater eutrophication）有所增加，原因是施肥策略的变化，包括减少氮肥使用和增加磷肥施用。敏感性分析证实了结果的一致性；尽管产量变化对结果有一定影响，但在所测试情景下，总体比较结果保持一致。这些发现凸显了智能与精准农业在真实农场条件下改善软质小麦生产环境可持续性的潜力。","Land","2026-09-04T00:00:00Z",75,{"impact":93,"substance":94,"depth":93,"authority":18,"freshness":53,"relevant":21,"comment":321},"基于真实农场数据的LCA研究，量化了精准农业转型的环境效益，对农业可持续发展具有参考价值。",[323],{"name":317,"url":314},[26,28,325,326,327],"精准农业","生命周期评价","意大利",{"code":52,"slug":104,"name":28,"path":105,"parent_code":106},[330],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":13,"version":40,"method":41,"evidence":332},{"candidates":333,"agricultural_evidence":46},[334,339,343,348,355,361,367],{"code":71,"score":16,"supported":46,"title":335,"abstract":337,"tags":338},[336],"precision agriculture",[325,336],[26,325],{"code":52,"score":125,"supported":46,"title":340,"abstract":341,"tags":342},[104],[28,104],[28],{"code":32,"score":95,"supported":46,"title":344,"abstract":345,"tags":347},[],[122,48,281,346],"fertilization",[],{"code":349,"score":20,"supported":46,"title":350,"abstract":351,"tags":354},"F4",[],[352,353],"农药","pesticide",[],{"code":174,"score":20,"supported":46,"title":356,"abstract":357,"tags":360},[],[358,359],"气候变化","climate change",[],{"code":362,"score":59,"supported":46,"title":363,"abstract":364,"tags":366},"F5",[],[365],"农机",[],{"code":368,"score":21,"supported":13,"title":369,"abstract":370,"tags":371},"H4",[],[],[326],[373,374],"生命周期评价 智慧农业 精准农业 意大利","生命周期评价 智慧农业","生命周期评价智慧农业精准农业意大利-1725","10.3390\u002Fland15091646",{"doi":376,"openalex_id":378,"authors":379,"venue":317,"cited_by_count":238,"oa_url":314,"card":398,"direction":243,"ingested_from":245},"W7208758947",[380,382,385,388,390,392,395],{"name":381,"orcid":8},"Claudia Camplone",{"name":383,"orcid":384},"Luisa Paolotti","https:\u002F\u002Forcid.org\u002F0000-0002-8264-1674",{"name":386,"orcid":387},"Francesco Romagnoli","https:\u002F\u002Forcid.org\u002F0000-0003-1814-7310",{"name":389,"orcid":8},"Sara Allahverdiveva",{"name":391,"orcid":8},"Renzo Santi",{"name":393,"orcid":394},"Lorenzo Covarelli","https:\u002F\u002Forcid.org\u002F0000-0002-4568-6160",{"name":396,"orcid":397},"Lucia Rocchi","https:\u002F\u002Forcid.org\u002F0000-0002-3386-2390",{"tldr":399,"method":400,"finding":401,"direction":402,"opportunity":403},"通过LCA评估意大利软小麦从传统向精准农业转型的环境影响，发现总影响降低20.8%。","基于农场实地数据，采用LCA（ISO标准）和Environmental Foot","精准农业结合机械更新使环境影响降低20.8%，但磷肥增加导致淡水富营养化上升。","农业绿色发展与碳","可研究精准农业下施肥策略优化，平衡产量与富营养化风险，或扩展至其他作物和地区。","2026-09-05T23:30:09.493129Z",{"id":406,"title":407,"url":408,"summary":409,"summary_zh":410,"content":8,"source_name":411,"source_url":408,"published_at":412,"category":90,"cover_url":8,"hotness":413,"is_selected":13,"score":414,"score_detail":415,"sources":417,"tags":421,"primary_topic":426,"secondary_topics":427,"classification":428,"search_phrases":457,"slug":460,"view_count":238,"doi":461,"paper":462,"created_at":473},5398,"Correlation Analysis of Banana Leaf Colour and Texture Features with Chemically Determined Nutrient Concentrations","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23470857","Correlation Analysis of Banana Leaf Colour and Texture Features with Chemically Determined Nutrient Concentrations examines the relationship between measurable colour and texture characteristics of banana leaves and their chemically determined nutrient concentrations. The study uses digital image processing to extract leaf features, including RGB colour values, colour intensity, texture patterns, and related statistical parameters. These image-based measurements are compared with laboratory-based nutrient analysis to identify significant correlations between optical features and nutrient levels. The research aims to determine whether leaf colour and texture can serve as reliable indicators of plant nutritional status. This approach may support the development of non-destructive, cost-effective, and efficient techniques for monitoring banana plant health. The findings can contribute to precision agriculture, improved nutrient management, and the advancement of image-based methods for crop assessment and agricultural research.","香蕉叶片颜色和纹理特征与化学测定养分浓度的相关性分析探讨了香蕉叶片可测量的颜色和纹理特征与其化学测定养分浓度之间的关系。该研究利用数字图像处理提取叶片特征，包括RGB颜色值、颜色强度、纹理模式及相关统计参数。将这些基于图像的测量结果与实验室养分分析进行比较，以识别光学特征与养分水平之间的显著相关性。该研究旨在确定叶片颜色和纹理能否作为植物营养状况的可靠指标。该方法可能有助于开发无损、经济高效且高效的香蕉植株健康监测技术。研究结果可为精准农业、改进养分管理以及推进基于图像的作物评估和农业研究方法作出贡献。","International Journal of Emerging Technologies and Innovative Research","2026-10-19T00:00:00Z",25,50,{"impact":285,"substance":16,"depth":18,"authority":53,"freshness":95,"relevant":21,"comment":416},"该研究探索用叶片颜色与纹理图像无损判断香蕉营养状况，思路有价值，但期刊权威性低、样本规模未明，暂不宜进入每日精选。",[418,419],{"name":411,"url":408},{"name":411,"url":420},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23471101",[26,422,423,424,425],"无损检测","图像识别","香蕉","营养诊断",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":429},{"candidates":430,"agricultural_evidence":46},[431,436,441,447,452],{"code":71,"score":432,"supported":46,"title":433,"abstract":434,"tags":435},7,[],[325,336],[26],{"code":32,"score":59,"supported":46,"title":437,"abstract":438,"tags":440},[],[439],"养分管理",[],{"code":442,"score":59,"supported":46,"title":443,"abstract":444,"tags":446},"I7",[],[445],"研究方法",[],{"code":448,"score":21,"supported":13,"title":449,"abstract":450,"tags":451},"B2",[],[],[424],{"code":453,"score":21,"supported":13,"title":454,"abstract":455,"tags":456},"I2",[],[],[423],[458,459],"香蕉叶片 营养浓度 图像特征","图像识别 无损检测 智慧农业 营养诊断","香蕉叶片营养浓度图像特征-5398","10.5281\u002Fzenodo.23470857",{"doi":461,"openalex_id":463,"authors":464,"venue":411,"cited_by_count":238,"oa_url":408,"card":467,"direction":472,"ingested_from":245},"W7222963764",[465],{"name":466,"orcid":8},"Anantjit Publication",{"tldr":468,"method":469,"finding":470,"direction":308,"opportunity":471},"分析香蕉叶片颜色与纹理特征同化学测定养分浓度的相关性。","数字图像处理提取RGB、颜色强度与纹理参数，与实验室养分分析对比。","叶片光学特征与养分水平存在显著相关，可作营养状态指标。","可扩展到多作物、多生育期及高光谱纹理融合的养分无损诊断模型构建。","数字乡村与农业信息化","2026-10-20T23:30:23.713550Z",{"id":475,"title":476,"url":477,"summary":478,"summary_zh":479,"content":8,"source_name":480,"source_url":477,"published_at":481,"category":90,"cover_url":8,"hotness":12,"is_selected":13,"score":482,"score_detail":483,"sources":486,"tags":488,"primary_topic":492,"secondary_topics":493,"classification":494,"search_phrases":510,"slug":513,"view_count":238,"doi":514,"paper":515,"created_at":553},5327,"Poloxamer‐Enabled Thermoresponsive Biobased Controlled‐Release Fertilizer for On‐Demand Nutrient Delivery","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fadvs.78371","ABSTRACT Synchronizing nutrient release of fertilizers with crop uptake remains a central challenge in precision agriculture. Here, we present a novel temperature‐responsive biobased controlled‐release fertilizer (TRBFs) incorporating Poloxamer 407 (F127) as a thermal switch within a castor oil‐derived polyurethane coating. The optimized TRBF10 (3 wt.% coating rate, 10 wt.% F127 in coating) exhibits a sharp, reversible phase transition at ∼20°C, matching the phenological requirements of overwintering crops. As quantified by digital refractometry, the 30‐day cumulative nitrogen release is suppressed to 9.22% at 10°C (crop dormancy) and increases 8.7‐fold to 80.32% at 30°C (active growth). This scalable technology overcomes intrinsic limitations of conventional diffusion‐controlled coated fertilizers and offers potential for on‐demand nutrient delivery with reduced environmental loss. Our work establishes sustainable strategies for climate‐adaptive precision agriculture and offers new insights and design paradigms for intelligent controlled‐release materials.","摘要 实现肥料养分释放与作物吸收的同步仍是精准农业的核心挑战。本研究提出一种新型温度响应型生物基控释肥料（TRBFs），在蓖麻油衍生聚氨酯包膜中引入泊洛沙姆407（F127）作为热开关。优化后的TRBF10（包膜率3 wt.%，包膜中F127含量10 wt.%）在约20°C时表现出急剧且可逆的相变，与越冬作物的物候需求相匹配。经数字折光法定量测定，30天累积氮释放量在10°C（作物休眠期）被抑制至9.22%，而在30°C（活跃生长期）增加8.7倍至80.32%。该可规模化技术克服了传统扩散控制型包膜肥料的内在局限，为按需养分供给并减少环境损失提供了潜力。本研究为气候适应性精准农业建立了可持续策略，并为智能控释材料提供了新见解与设计范式。","Advanced Science","2026-10-18T00:00:00Z",81,{"impact":93,"substance":94,"depth":93,"authority":484,"freshness":95,"relevant":21,"comment":485},14,"该研究提出温敏生物基控释肥，实现按需养分释放，方法新颖、数据扎实，对气候适应性精准农业有参考价值。",[487],{"name":480,"url":477},[26,325,489,490,491],"控释肥","温度响应","蓖麻油基材料",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":495},{"candidates":496,"agricultural_evidence":46},[497,501,505],{"code":32,"score":484,"supported":46,"title":498,"abstract":499,"tags":500},[281],[48,281],[],{"code":71,"score":285,"supported":46,"title":502,"abstract":503,"tags":504},[],[325,336],[26,325],{"code":174,"score":59,"supported":46,"title":506,"abstract":507,"tags":509},[],[508],"气候适应",[],[511,512],"Poloxamer 温敏控释肥","蓖麻油基材料 智慧农业 温度响应 精准农业","Poloxamer温敏控释肥-5327","10.1002\u002Fadvs.78371",{"doi":514,"openalex_id":516,"authors":517,"venue":480,"cited_by_count":238,"oa_url":477,"card":548,"direction":472,"ingested_from":245},"W7222872607",[518,520,523,525,527,530,532,534,536,539,542,545],{"name":519,"orcid":8},"Jiahui Liu",{"name":521,"orcid":522},"Li J","https:\u002F\u002Forcid.org\u002F0009-0008-8760-1365",{"name":524,"orcid":8},"Ziyao Li",{"name":526,"orcid":8},"Xiangjie Zhao",{"name":528,"orcid":529},"Fengyan Fang","https:\u002F\u002Forcid.org\u002F0009-0009-0081-4796",{"name":531,"orcid":8},"Yan Liu",{"name":533,"orcid":8},"Songyuan Wang",{"name":535,"orcid":8},"Boning Su",{"name":537,"orcid":538},"Yuanyuan Yao","https:\u002F\u002Forcid.org\u002F0000-0002-7439-2970",{"name":540,"orcid":541},"Bin Gao","https:\u002F\u002Forcid.org\u002F0000-0003-3769-0191",{"name":543,"orcid":544},"Shugang Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-1428-9082",{"name":546,"orcid":547},"Yuechao Yang","https:\u002F\u002Forcid.org\u002F0000-0003-4045-0252",{"tldr":549,"method":550,"finding":551,"direction":243,"opportunity":552},"研发含F127温敏开关的生物基包膜控释肥，实现按温度按需释放养分。","蓖麻油基聚氨酯包膜中嵌入Poloxamer 407作热开关，数字折光法测氮释放。","10℃时30天氮累积释放仅9.22%，30℃时增至80.32%，提升8.7倍。","可探索多响应（温湿\u002FpH）智能包膜与田间作物需肥曲线耦合的精准释放调控。","2026-10-19T23:30:28.317764Z",{"id":555,"title":556,"url":557,"summary":558,"summary_zh":559,"content":8,"source_name":560,"source_url":557,"published_at":561,"category":90,"cover_url":8,"hotness":12,"is_selected":13,"score":562,"score_detail":563,"sources":565,"tags":567,"primary_topic":572,"secondary_topics":573,"classification":574,"search_phrases":616,"slug":619,"view_count":66,"doi":620,"paper":621,"created_at":633},5235,"Role of Smart Fertilization in Modern Agriculture","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fijpss\u002F2026\u002Fv38i106378","Global food demand is increasing rapidly owing to population growth, placing considerable pressure on agricultural systems to enhance productivity under constraints on natural resources. Although conventional fertiliser use has contributed significantly to improved yields, its excessive and imbalanced application has resulted in soil degradation, nutrient leaching, groundwater contamination, and increased emissions of nitrous oxide (N₂O), thereby reducing nutrient use efficiency (NUE). In this context, smart fertilisation has emerged as a science-driven nutrient management strategy that emphasises the application of the right nutrient source at the right rate, time and place (4R principle), supported by advanced tools such as soil and plant sensors, remote sensing, geographic information systems, and decision-support platforms. Key components include controlled-release fertilisers, nano-fertilisers, bio-formulations, fertigation systems, variable rate technology (VRT), and unmanned aerial vehicle (UAV)-based crop monitoring. Evidence from multi-location field studies across diverse agro-climatic regions of India and other countries indicates that smart fertilisation can enhance nutrient use efficiency by 50–80%, reduce fertiliser input by 19–30%, and improve crop yields by 22–30% in major crops such as rice, wheat, maize and pulses. With the global fertiliser market exceeding USD 200 billion, the adoption of precision nutrient management technologies is gaining momentum. Overall, smart fertilisation offers a sustainable intensification pathway by improving productivity while simultaneously minimising environmental externalities. However, challenges related to technology accessibility, cost, and farmer adoption remain critical constraints that require policy and institutional support.","由于人口增长，全球粮食需求迅速上升，给农业系统带来了巨大压力，要求在自然资源约束下提高生产力。尽管常规施肥对提高产量作出了显著贡献，但过量和不平衡施肥已导致土壤退化、养分淋失、地下水污染以及氧化亚氮（N₂O）排放增加，从而降低了养分利用效率（NUE）。在此背景下，智能施肥作为一种以科学为驱动的养分管理策略应运而生，强调在正确的时间、正确的地点以正确的用量施用正确的养分来源（4R原则），并借助土壤与植物传感器、遥感、地理信息系统和决策支持平台等先进工具加以支撑。其关键组成部分包括控释肥料、纳米肥料、生物制剂、水肥一体化系统、变量施肥技术（VRT）以及基于无人机（UAV）的作物监测。来自印度及其他国家不同农业气候区多点田间研究的数据表明，智能施肥可使养分利用效率提高50%–80%，减少肥料投入19%–30%，并使水稻、小麦、玉米和豆类等主要作物增产22%–30%。随着全球肥料市场规模超过2000亿美元，精准养分管理技术的采用正在加速推进。总体而言，智能施肥通过提高生产力同时最大限度减少环境负外部性，提供了一条可持续集约化路径。然而，技术可及性、成本和农民采用等方面的挑战仍是关键制约因素，需要政策和制度支持。","International Journal of Plant & Soil Science","2026-10-17T00:00:00Z",83,{"impact":94,"substance":94,"depth":93,"authority":18,"freshness":285,"relevant":21,"comment":564},"系统综述智能施肥技术体系与多国田间数据，量化增产节肥效果，对智慧农业与绿色投入品方向有参考价值，但属综述性论文且非国内案例，时效与权威度中等。",[566],{"name":560,"url":557},[26,568,100,569,570,571],"无人机","变量施肥","遥感监测","养分利用效率",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":575},{"candidates":576,"agricultural_evidence":46},[577,582,586,593,597,601,605,612],{"code":32,"score":578,"supported":46,"title":579,"abstract":580,"tags":581},20,[346],[122,48,102,439,120],[571,569,100],{"code":267,"score":432,"supported":46,"title":583,"abstract":584,"tags":585},[],[261,288],[570],{"code":587,"score":20,"supported":46,"title":588,"abstract":589,"tags":592},"A1",[],[590,591],"水稻","rice",[],{"code":52,"score":20,"supported":46,"title":594,"abstract":595,"tags":596},[],[28,104],[],{"code":135,"score":20,"supported":46,"title":598,"abstract":599,"tags":600},[],[138,139],[],{"code":148,"score":20,"supported":46,"title":602,"abstract":603,"tags":604},[],[151,152],[],{"code":606,"score":20,"supported":46,"title":607,"abstract":608,"tags":611},"I4",[],[609,610],"地理信息","geographic information",[],{"code":71,"score":21,"supported":13,"title":613,"abstract":614,"tags":615},[],[],[26],[617,618],"智能施肥 4R原则","变量施肥技术 无人机","智能施肥4R原则-5235","10.9734\u002Fijpss\u002F2026\u002Fv38i106378",{"doi":620,"openalex_id":622,"authors":623,"venue":560,"cited_by_count":238,"oa_url":557,"card":628,"direction":243,"ingested_from":245},"W7222838957",[624,626],{"name":625,"orcid":8},"S. T. Panusha",{"name":627,"orcid":8},"T. M. Soumya",{"tldr":629,"method":630,"finding":631,"direction":243,"opportunity":632},"综述智能施肥（4R原则+传感器\u002F遥感\u002FVRT）提升养分利用效率与产量的机制与证据。","整合多地点田间试验证据，梳理传感器、遥感、GIS、控释\u002F纳米肥与无人机监测等技术","智能施肥可提高养分利用效率50–80%、减施化肥19–30%、增产22–30%，但受成本与农户采纳制","可研究低成本传感器+无人机变量施肥在小农户场景的适配与采纳机制，及减氮减排的量化评估。","2026-10-18T23:30:07.850055Z"]